Power load forecasting in Shanghai based on CNN-LSTM combined model

Jiahua Liu; Yufei Nie; Yonggeng Lu

doi:10.1117/12.2640046

23 May 2022 Power load forecasting in Shanghai based on CNN-LSTM combined model

Jiahua Liu, Yufei Nie, Yonggeng Lu

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 122540K (2022) https://doi.org/10.1117/12.2640046
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

Load forecasting is the basis of economic operation of power system. In the time series forecasting method based on deep neural network, single load forecasting method can not meet the requirements of load forecasting in the new period. Therefore, a short-term power load forecasting method based on CNN-LSTM neural network is adopted in this paper. This paper first introduces the data set used and the pre-processing operation of the data sample. Then, a sequential hybrid model consisting of single-layer CNN and two-layer LSTM is designed. Finally, the experimental results obtained by using the hybrid model are compared with those obtained by using CNN alone and LSTM alone. The experimental results show that the power load prediction method proposed in this paper gives full play to the advantages of multiple models, effectively deals with the linear and nonlinear characteristics of data samples, and further improves the accuracy of load prediction.

Citation Download Citation

Jiahua Liu, Yufei Nie, and Yonggeng Lu "Power load forecasting in Shanghai based on CNN-LSTM combined model", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 122540K (23 May 2022); https://doi.org/10.1117/12.2640046

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